To compare the biometric data relating to two individuals and determine whether they are the same person (the term “duplicate” then applying) or different people (non-duplicates), several digital data items can be available. These correspond, for example, to the comparison scores of each of their ten fingers. The present application is more particularly interested in the merging of the scores of these data items, in order to best make the duplicate/non-duplicate decision. The usual comparison performance measurements are error ratios, namely:                The FAR (False Acceptance Rate), which is a “duplicate” classification rate for data concerning individuals who are in reality different,        The FRR (False Rejection Rate) which is a “non-duplicate” classification rate for the data in fact belonging to one and the same individual.        
When a large number of different comparison scores have to be processed, for example those relating to the ten fingers of an individual, in order for a single decision to be made, these scores are merged. In this case, the merging operator is effective if, for a given FAR, it minimizes the FRR (or conversely, if for a given FRR, it minimizes the FAR).
To perform the merge, the geometric mean m of the comparison scores of each of the ten fingers is calculated. Using a simple comparison of m with a threshold, the “duplicate” or “non-duplicate” decision is made. The threshold is determined by trial and error from measurements made on a sample of data. Such a known method does, however, have the following drawbacks:                It deals badly with the case where certain digital data is not available (for example, because it is not possible to acquire the image of the prints of certain fingers).        It is applied ineffectively to the scores supplied by certain comparison operators. For example, in the case of two operators, it imposes a hyperbola branch as the decision boundary, which does not always make it possible to obtain an optimum solution.        
It presupposes that the various comparison operators supply uniform scores, which is not, for example, the case if fingerprint scores are to be merged with facial recognition scores.